A FUZZY LOGIC CLASSIFIER DESIGN FOR ENHANCING BCI PERFORMANCE

Abstract

This work is aimed at enhancing inter-session performance of Brain-Computer Interface (BCI) classification. The effective handling of uncertainties associated with changing brain dynamics is considered to be a key issue. Since fuzzy logic (FL) has been recognized as a functional and well-suited approach to capturing the effects of uncertainty, the research has been concentrated on the development of an FL classifier for a BCI system. The emphasis is placed on type-2 (T2) FL methodology that has recently emerged as an expanded version of classical type-1 (T1) FL. In this work a case study was conducted using ECoG recordings made available as part of BCI competition III. Due to high dimensionality of the signal, two-stage feature selection was devised. The overall performance of the developed BCI was assessed in off-line simulations based on the classification accuracy (CA). Comparative analysis of the designed T2FL and T1FL systems with LDA as BCI classifiers suggests that T2FL has superior capability in effective dealing with inter-session variability of the ECoG dynamics in the given subject.

abstract = "This work is aimed at enhancing inter-session performance of Brain-Computer Interface (BCI) classification. The effective handling of uncertainties associated with changing brain dynamics is considered to be a key issue. Since fuzzy logic (FL) has been recognized as a functional and well-suited approach to capturing the effects of uncertainty, the research has been concentrated on the development of an FL classifier for a BCI system. The emphasis is placed on type-2 (T2) FL methodology that has recently emerged as an expanded version of classical type-1 (T1) FL. In this work a case study was conducted using ECoG recordings made available as part of BCI competition III. Due to high dimensionality of the signal, two-stage feature selection was devised. The overall performance of the developed BCI was assessed in off-line simulations based on the classification accuracy (CA). Comparative analysis of the designed T2FL and T1FL systems with LDA as BCI classifiers suggests that T2FL has superior capability in effective dealing with inter-session variability of the ECoG dynamics in the given subject.",

N2 - This work is aimed at enhancing inter-session performance of Brain-Computer Interface (BCI) classification. The effective handling of uncertainties associated with changing brain dynamics is considered to be a key issue. Since fuzzy logic (FL) has been recognized as a functional and well-suited approach to capturing the effects of uncertainty, the research has been concentrated on the development of an FL classifier for a BCI system. The emphasis is placed on type-2 (T2) FL methodology that has recently emerged as an expanded version of classical type-1 (T1) FL. In this work a case study was conducted using ECoG recordings made available as part of BCI competition III. Due to high dimensionality of the signal, two-stage feature selection was devised. The overall performance of the developed BCI was assessed in off-line simulations based on the classification accuracy (CA). Comparative analysis of the designed T2FL and T1FL systems with LDA as BCI classifiers suggests that T2FL has superior capability in effective dealing with inter-session variability of the ECoG dynamics in the given subject.

AB - This work is aimed at enhancing inter-session performance of Brain-Computer Interface (BCI) classification. The effective handling of uncertainties associated with changing brain dynamics is considered to be a key issue. Since fuzzy logic (FL) has been recognized as a functional and well-suited approach to capturing the effects of uncertainty, the research has been concentrated on the development of an FL classifier for a BCI system. The emphasis is placed on type-2 (T2) FL methodology that has recently emerged as an expanded version of classical type-1 (T1) FL. In this work a case study was conducted using ECoG recordings made available as part of BCI competition III. Due to high dimensionality of the signal, two-stage feature selection was devised. The overall performance of the developed BCI was assessed in off-line simulations based on the classification accuracy (CA). Comparative analysis of the designed T2FL and T1FL systems with LDA as BCI classifiers suggests that T2FL has superior capability in effective dealing with inter-session variability of the ECoG dynamics in the given subject.